Grok-4 vs Kimi K3
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Grok-4 by xAI against Kimi K3 by Moonshot AI across real-world coding, reasoning, token economics, and latency.
원하는 AI 모델 선택 및 실시간 비교
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Live comparison: no in-depth editorial review has been published for this pair yet.
Grok-4
플래그십 프론티어 모델xAI의 Grok-4 모델은 최첨단 엔터프라이즈 추론, 소프트웨어 개발 및 핵심 프로덕션 워크로드에 최적화된 AI 모델로, 1M 컨텍스트 윈도우, 초당 125 토큰의 생성 속도, 종합 지능 지수 92/100점(1M 토큰당 입력 $2.5 / 출력 $10)을 제공합니다.
$2.50 / 1M
$10.00 / 1M
1M tokens
76.5%
Kimi K3
오픈 웨이트 추론 모델Moonshot AI의 Kimi K3 모델은 강화학습 기반의 고효율 오픈 웨이트 사고 사슬(Chain-of-Thought) 추론에 최적화된 AI 모델로, 1.0M 컨텍스트 윈도우, 초당 58 토큰의 생성 속도, 종합 지능 지수 93/100점(무료 오픈 웨이트 제공)을 제공합니다.
$0.00 / 1M
$0.00 / 1M
1.05M tokens
74.5%
한눈에 보는 핵심 차이점
- Kimi K3 is 1.0x cheaper for API usage (input + output tokens combined).
- Kimi K3 has a larger context window (1M vs 1M tokens).
- Grok-4 is faster at 125 tok/s vs 58 tok/s.
- Grok-4 leads on SWE-bench coding (76.5% vs 74.5%).
| Feature | Grok-4 | Kimi K3 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 92 | 93 |
Provider | xAI | Moonshot AI |
Input Price Cost per million input tokens | $2.50 / 1M | $0.00 / 1M |
Output Price Cost per million output tokens | $10.00 / 1M | $0.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $1.68 / 1M | $0.00 / 1M |
Context Window Maximum input + output tokens | 1M tokens~1,500 A4 pages of text | 1.05M tokens~1,573 A4 pages of text |
Max Output Tokens Per single request | 64K | 131K |
Throughput Speed Tokens generated per second | 125 tok/s | 58 tok/s |
SWE-bench Verified Real-world software engineering | 76.5% | 74.5% |
GPQA Diamond Graduate-level reasoning | 77.8% | 79% |
Cached Input Price Prompt caching discount per 1M tokens | $0.25 / 1M | $0.00 / 1M |
부문별 우위 모델
At-a-glance decision framework검증된 벤치마크 점수 비교
Official evaluation metrics verified by independent third-party research labs.
Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.
Challenging questions created by domain experts to test complex PhD-level reasoning.
Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.
Crowdsourced human preference evaluation based on blind, head-to-head model battles.
Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.
Autonomous multi-step tasks executed inside a real command-line terminal environment.
Research-grade coding problems drawn from real scientific and numerical workloads.
Contamination-free competitive programming problems collected continuously over time.
American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.
Accuracy when reasoning over very large inputs that fill the model context window.
Tokens emitted per second during active generation streaming.
지연 시간(Latency) 및 응답 속도
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
실시간 API 토큰 비용 시뮬레이터
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Grok-4 · Strengths & Key Advantages
- 1M 컨텍스트 윈도우 및 최대 64K 출력 토큰 생성 지원 (SWE-bench Verified 76.5% · GPQA Diamond 77.8%)
- 초당 125 토큰의 빠른 처리 속도와 680ms에 불과한 첫 토큰 지연 시간(TTFT)
- 합리적인 API 요금 체계(1M 토큰당 입력 $2.5 / 출력 $10)와 프롬프트 캐싱, 함수 호출 및 정형화된 JSON 출력 완벽 지원
Considerations & Trade-offs
- 독점 클라우드 전용 모델로 로컬 오프라인 서버에 가중치를 다운로드하여 실행할 수 없습니다
- 대규모 프로덕션 호출 환경에서는 비용 절감을 위해 프롬프트 캐싱과 배치 API(Batch API) 활용이 권장됩니다
Kimi K3 · Strengths & Key Advantages
- 1.0M 컨텍스트 윈도우 및 최대 131K 출력 토큰 생성 지원 (SWE-bench Verified 74.5% · GPQA Diamond 79%)
- 초당 58 토큰의 빠른 처리 속도와 720ms에 불과한 첫 토큰 지연 시간(TTFT)
- 오픈 웨이트 아키텍처 (2.8T total (MoE))를 통해 vLLM, SGLang, Ollama 기반 자체 GPU 서버 구축 및 파인튜닝 가능
Considerations & Trade-offs
- 비양자화 전체 정밀도로 로컬 서버에서 구동하려면 충분한 GPU VRAM 용량이 필요합니다
- 초고난도 학술 연구 및 수학 증명 과제에서는 상위 대형 추론 특화 모델이 더 깊은 분석을 제공할 수 있습니다
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Grok-4 | Kimi K3 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $1.65 | $2.00 |
Output Tokens / Task Tokens generated per task | 48K | 35K |
Reasoning Tokens / Task Thinking tokens burned per task | 24K | 18K |
기술 사양 (Specifications)
Architecture, release timeline, capabilities, and modalities.
| Feature | Grok-4 | Kimi K3 |
|---|---|---|
| Official API Model ID (SDK) | grok-4-latest | kimi-k3 |
| API Protocol | xAI REST API (OpenAI-Compatible) | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2026-07 | 2026-07 |
| Knowledge Cutoff | Real-time (Up to October 2026) | March 2026 |
| Architecture | Colossus-Scale Dense Mixture-of-Experts | Colossus-Scale Multimodal Sparse Mixture-of-Experts |
| Parameters | Confidential | 2.8T total (MoE) |
| Enterprise Rate Limits (RPM / TPM) | 5,000 – 15,000 RPM (Production Tier) · 4,000,000 – 10,000,000 TPM | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud |
| Long-Context Tier (>128K/200K) | Flat rate ($2.5/1M in) across full 1000K window | Free across full context window (only KV-cache VRAM scales) |
| Prompt Cache Policy & TTL | Cached input at $0.25/1M tokens | Automatic prefix KV-caching in vLLM / SGLang (0 cost) |
| Computer Use / GUI Agent | Supported (Screen coordinate grounding, browser & desktop GUI action loops) | Supported (Screen coordinate grounding, browser & desktop GUI action loops) |
| MCP & Parallel Tool Calling | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema |
| Web Search Grounding & Sandbox | Real-time Web & X (Twitter) Search Grounding + Code Execution | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools |
| Official Fine-Tuning Support | API Prompt Engineering & RAG Distillation | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) |
| Multimodal Token Billing | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) |
| Quantization & Min GPU VRAM | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 1736 GB VRAM (4-bit Quantized) · 6020 GB VRAM (Native BF16) |
| Safety & Frontier Classification | xAI Production Safety & Alignment Verified | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control |
| Input Modalities | text, vision | text, vision |
| Output Modalities | text | text |
Grok-4 vs Kimi K3: 핵심 요약 문답 (FAQ)
Which is more intelligent, Grok-4 or Kimi K3?
Kimi K3 is more intelligent. It scores 93 compared with 92 on the composite Intelligence Index.
Which model is faster?
Grok-4 is faster, generating 125 tokens per second compared with 58 tokens per second.
Which model is cheaper to run?
Kimi K3 is cheaper at $0.00 per 1M tokens blended, compared with $1.68 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Grok-4 has lower latency, with a time to first token of 0.68s compared with 0.72s.
Which model has a larger context window?
Kimi K3 has a larger context window, supporting 1.05M tokens compared with 1M tokens.
종합 평가
Choose Grok-4 for 최첨단 엔터프라이즈 추론, 소프트웨어 개발 및 핵심 프로덕션 워크로드, 엔터프라이즈 rag 지식베이스 및 대규모 프로덕션 api 연동에 최적., or choose Kimi K3 for 강화학습 기반의 고효율 오픈 웨이트 사고 사슬(chain-of-thought) 추론, 엔터프라이즈 rag 지식베이스 및 대규모 프로덕션 api 연동에 최적..
자주 묻는 질문 (FAQ)
Which is cheaper: Grok-4 or Kimi K3?+
Grok-4 costs $2.5/1M input and $10/1M output tokens, while Kimi K3 costs $0/1M input and $0/1M output tokens.
Which model has a larger context window: Grok-4 or Kimi K3?+
Grok-4 supports 1M tokens whereas Kimi K3 supports 1.0M tokens.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
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Benchmark scores, token prices, and rate limits evolve rapidly as AI labs release model updates. Always verify current specifications and pricing on the official provider documentation.
공식 출처: xAI & Moonshot AI Official Documentation